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All Journal Telcomatics Jurnal UNITEK Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Sistem Informasi Mahakarya (JSIM) Journal of Information System and Education Development JAR'S (Journal of Advanced Research in Informatics) International Journal of Applied Mathematics and Computing. International Journal of Mechanical, Industrial and Control Systems Engineering JIeTri : Journal of Industrial Engineering Tridinanti Paidagogia Green Engineering: Journal of Engineering and Applied Science JBIAM Global Science: Journal of Information Technology and Computer Science
Trisnawan, Ahmad Budi
Universitas Mahakarya Asia
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pemanfaatan Machine Learning untuk Peningkatan Akurasi Sistem Pendukung Keputusan Prediktif Ahmad Budi Trisnawan; Tuti Susilawati
JURNAL UNITEK Vol. 18 No. 2 (2025): Juli-Desember 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i2.1702

Abstract

The rapid development of information technology and the increasing availability of large-scale data have driven the need for decision-making systems that are more intelligent, faster, and more accurate. Conventional Decision Support Systems (DSS) generally rely on rule-based approaches or simple statistical analyses, which have limitations in recognizing complex patterns and are less adaptive to changes in data. Therefore, the integration of machine learning technology represents a strategic solution to enhance the predictive capability and decision quality produced by DSS. This study aims to analyze the utilization of machine learning algorithms in improving the accuracy of predictive decision support systems. The method employed is a comparative experimental approach involving three algorithms, namely Decision Tree, Random Forest, and Support Vector Machine. The dataset used consists of historical decision outcomes along with their determining variables derived from a case study. The research stages include data cleaning, normalization, training–testing set splitting, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of machine learning significantly improves DSS accuracy compared to conventional methods. Random Forest achieved the best performance with an accuracy of 91%, followed by Support Vector Machine at 87% and Decision Tree at 84%. In addition to improving accuracy, the integration of machine learning also enhances data processing efficiency and decision-making speed. These findings demonstrate that artificial intelligence–based DSS has great potential for application across various domains, such as business, healthcare, education, and government.
Adaptive Edge-AI Framework for Real-Time Cyber-Physical Systems in Smart Cities with Resource-Constrained IoT Devices Benny Martha Dinata; Ahmad Budi Trisnawan; Eram Abbasi
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.170

Abstract

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.
Integrasi Big Data dan Sistem Informatika Manufaktur dalam Prediksi Permintaan Produksi Ahmad Budi Trisnawan Wawan
JURNAL TEKNIK DAN SISTEM INDUSTRI Vol 3 No 01 (2025): Edisi Januari-Juni
Publisher : PROGRAM STUDI TEKNIK INDUSTRI UNIVERSITAS TRIDINANTI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dalam era industri 4.0, kemampuan untuk mengelola dan menganalisis data dalam skala besar menjadi kebutuhan utama bagi sektor manufaktur yang ingin meningkatkan efisiensi dan daya saing. Penelitian ini mengkaji integrasi teknologi Big Data dengan sistem informatika manufaktur untuk mendukung proses prediksi permintaan produksi secara lebih akurat dan adaptif. Sistem informatika manufaktur konvensional sering kali terbatas dalam mengolah data real-time yang berasal dari berbagai sumber seperti histori penjualan, tren pasar, kondisi cuaca, hingga perilaku konsumen digital. Dengan memanfaatkan arsitektur Big Data, data dalam volume besar dapat dikumpulkan, disimpan, dan dianalisis secara cepat menggunakan algoritma prediktif berbasis machine learning, seperti Random Forest dan Long Short-Term Memory (LSTM). Studi ini menunjukkan bahwa integrasi ini tidak hanya meningkatkan akurasi prediksi permintaan, tetapi juga membantu pengambilan keputusan dalam penjadwalan produksi, pengelolaan persediaan, dan distribusi. Hasil evaluasi model prediktif memperlihatkan peningkatan akurasi hingga lebih dari 90% dibandingkan metode tradisional. Implementasi sistem ini juga memberikan dampak signifikan terhadap pengurangan biaya operasional dan peningkatan responsivitas terhadap perubahan pasar. Temuan ini mempertegas pentingnya transformasi digital berbasis data dalam mendukung sistem manufaktur yang cerdas dan adaptif.
Energy Aware Software Architecture Optimization Using Real Time Analytics and Self Adaptive Control in Intelligent Computing Systems Ardy Wicaksono; Mursalim Mursalim; Arif Tri Widiyatmoko; Deny Prasetyo; Ahmad Budi Trisnawan; Yanuar Wicaksono
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.195

Abstract

The increasing demand for intelligent computing systems, including cloud computing, artificial intelligence (AI), and the Internet of Things (IoT), has resulted in a significant rise in energy consumption, which poses both environmental and economic challenges. The high computational power required by these systems, coupled with the continuous operation of data centers and connected devices, has led to inefficiencies in energy usage. This paper explores the integration of real time analytics and self adaptive control mechanisms to optimize energy consumption in intelligent systems. By employing advanced software tools for real time monitoring, dynamic adjustments based on workload conditions, and adaptive algorithms for energy optimization, significant reductions in power usage were achieved without compromising system performance. The optimized architecture dynamically adjusts system parameters such as processor frequency, task scheduling, and voltage to ensure efficient energy consumption during varying operational demands. The results show a 24% reduction in energy usage during low demand periods, demonstrating the potential of real time energy management strategies. The study also compares the optimized architecture with conventional static systems, highlighting the benefits of dynamic energy management, including improved performance balance, reduced environmental impact, and lower operational costs. These findings suggest that the integration of energy efficient practices in software design, particularly through real time analytics and self adaptive mechanisms, offers a sustainable solution for modern computing systems. Future research could focus on improving self adaptive systems, incorporating renewable energy sources, and expanding the approach to other intelligent systems, such as autonomous vehicles or large scale smart grids. The practical applications of this research are vast, particularly in large scale applications such as data centers and cloud computing, where energy efficiency is critical for sustainability.
Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.288

Abstract

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery Suyahman Suyahman; Deny Prasetyo; Ahmad Budi Trisnawan; Ardy Wicaksono; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.402

Abstract

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.
Co-Authors Agus Wantoro Ardy Wicaksono Ardy Wicaksono Arif Tri Widiyatmoko Benny Martha Dinata Deny Prasetyo Deny Prasetyo Dewi Mulyama Dian Afriady Elkin Rilvani Eram Abbasi Erlita Sulistiati Erlita Sulistiati Fahlul Rizki Ferly Ardhy Muhamad Furqon Mursalim Mursalim Nur Amalia Priasnyomo Prima Santoso Rachmat Setiabudi Rahayu, Tri Mulyaningtyas Sinatrya, Yuanita Suryadi Suryadi Suyahman Suyahman Syed Asif Ali TUTI SUSILAWATI Yanuar Wicaksono Yulaikha Mar’atullatifah